6 Sectors Embracing AI & ML Technology
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly becoming key drivers of growth and innovation across a range of industries. From many sci-fi films and literature, it’s easy to only associate AI with super futuristic projects, such as space travel and robotics. But in reality, there are many simple and practical ways to implement AI in our day-to-day lives, which have huge benefits for businesses, governments, and ordinary people alike.
Here we take a closer look at how AI and ML are being implemented in new and exciting ways across all types of sectors. But first…
What are AI & ML?
Artificial Intelligence and Machine Learning are two correlating branches of computer science, which are attempting to simulate human thinking and behaviour. While the terms are sometimes used interchangeably, there are some noticeable differences between the two. In fact, ML is considered a subfield of AI.
AI’s main objective is to build smart computer systems that are capable of solving complex problems and thinking in human-like ways. The field of Machine Learning, on the other hand, is about creating machine systems that are able to learn from past data on their own accord, without being explicitly programmed. Through analysing this historical data, ML systems can then perform a number of tasks, including identifying patterns, making predictions, and taking autonomous decisions.
These two approaches to computer technology can help create solutions and improve a wide variety of issues in many industries.
Educators are beginning to embrace AI in order to assist with providing more personalised learning experiences for pupils based on their individual capabilities, ambitions, and needs. The beauty of AI is its ability to adapt to each student’s level of knowledge, speed of learning, and desired goals, so that they receive a more tailored education. AI-powered adaptive learning systems are able to deliver predictive insights to identify a range of patterns in students, from previous learning histories and subject weaknesses to trends in early school-leavers. Data gathered from AI adaptive learning systems will help educators take preventative actions to combat any noticeable problems while enhancing the overall education system for the benefit of all students.
The tourism industry is embracing AI technology to offer more immersive and personalised experiences to travellers. In Malta, for example, the government is hoping to drive its tourism industry into the future through the introduction of a Digital Tourism Platform. The goal of this project is to gather insightful data through interactive information kiosks set up around Malta’s tourism hotspots. This data will be used to create tourist personas which will enable the platform to suggest itineraries and experiences to tourists that cater to their likes and interests. Other tourism-related projects embracing AI include the development of immersive and educational virtual and augmented reality (AR) experiences at various attractions, museums, and historical sites.
Several successful pilot projects conducted in the US, the UK, Germany, and Singapore have shown that Traffic AI is capable of slashing waiting times at traffic light junctions, reducing congestion and emissions, and improving road safety. Simulations run in Hagen, Germany for example, have shown that AI-optimised traffic lights can reduce waiting times by up to 47% compared to a traditional pre-timed signal plan.
More countries are planning to explore how AI can be incorporated into their traffic control systems and geographic information systems (GISs). AI-managed traffic light control systems will work in conjunction with mobility analytics dashboards to conduct real-time vehicle tracking. This will enable AI-powered journey planner applications to more accurately identify patterns in transport behaviours, suggest optimised routes, and indicate the expected time to reach destinations.
The potential for AI and deep-learning systems in healthcare is vast. AI has the ability to help healthcare professionals provide more efficient care, diagnose problems, and gather genetic data that can be used to more accurately identify diseases, such as cancer, earlier on. By implementing deep learning systems within healthcare data repositories, AI can assist medical practitioners make more informed decisions on patient safety; perform predictive analysis to decrease medical costs; and devise preventive care models for individual patients as well as for specific demographics of people as a whole.
Chatbots and similar AI-powered customer service solutions are becoming more ubiquitous across public and private sector websites. The main benefit of a chatbot is its facility to resolve simple questions quickly. Consequently, they are able to help businesses save on operational costs while enhancing the customer service experience. Currently, such AI systems, including AI-driven email assistants, are already being introduced across public sector information websites and through the financial industry, which are able to suggest automatic replies to speed up query response times. AI systems first monitor how human agents respond to emails and learn how to better formulate email responses that accurately answer queries. More advanced projects are aiming to develop chatbot and telephone voice assistant systems capable of directing people to the information they are seeking.
From data collection and analysis to the recommendation of actionable insights, AI and machine learning algorithms are redefining how utility companies operate across the world. Utilities-focused AI algorithms collect, organise, and analyse tons of data collected by national water, electric, and gas companies. The AI system’s goal is to discover patterns and other useful information relating to utility usage. This can help utility companies maximise resources and provide responsive real-time customer service. Such large-scale projects are expected to drive better efficiency, resilience, and stability across energy and water networks, and lay the foundation for the next evolution of smart grid networks.